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In the analysis, we compared the performance of prediction models using data sets with and without explicit representations of the absence of data elements. 1 Synthetic Data Whittaker reports a data set [20] that involves six boolean risk factors X1 , . . , X6 observed in a sample of 1841 employees of a Czech car factory. Ramoni and Sebastiani [9] considered these data and used a structure learning algorithm to output Exploiting Data Missingness in Bayesian Network Modeling 41 a structure that they used afterwards as a toy problem to learn the conditional probability tables from incomplete data sets.

Detecting fuzzy communities in complex networks with a potts model. Phys. Rev. Lett. 93 (2004) 20. : Defining and identifying communities in networks. Publ. Natl. Acad. Sci. USA 101, 2658–2663 (2004) 21. : Optimization by simulated annealing. Science 220, 671 (1983) 22. : Community structure in social and biological networks. hk Abstract. Data streams are dynamic, with frequent distributional changes. In this paper, we propose a statistical approach to detecting distributional shifts in multi-dimensional data streams.

U is composed by all the original random variables (Xi ) and artificially created variables (Ri ) representing missingness of their respective random variables. MBLearning is then first run on the target variable T (line 2). It is then run again repeatedly on the adjacent nodes and so on up to a radius of r around the target node (lines 5-13). A similar approach was proposed in [19], but it does not take into account missingness as a possible piece of information. After finishing the feature subset selection process, GMB creates at line 14 the local BN including the selection of existing and the dummy variables.

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